📅 2024-09-10 — Session: Analyzed and Visualized Firm Profit Strategies
🕒 00:25–00:45
🏷️ Labels: Game Theory, Profit Analysis, Python, Visualization, Collusion, Nash Equilibrium
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal: The session aimed to analyze and visualize firm profit strategies within the context of game theory, focusing on collusion, Nash equilibrium, and deviation strategies.
Key Activities:
- Conducted a detailed analysis of collusive profits and identified issues with negative profits, suggesting model assumption adjustments for realistic conditions.
- Developed Python code using matplotlib to visualize profits for two firms under different strategic scenarios, including Nash equilibrium, collusive, and deviation strategies.
- Adjusted profit calculations based on output quantities, ensuring accurate representation of Nash, collusive, and deviation profits.
- Simulated a two-spell duration model using a Weibull baseline hazard, generating covariates, parameters, and random effects with Python code.
- Analyzed the impact of random effects and covariates on individual hazard rates in a Weibull distribution model.
Achievements:
- Successfully visualized firm profits under various strategic scenarios using Python.
- Enhanced understanding of profit dynamics in game theory models through simulation and analysis.
Pending Tasks:
- Further refine model assumptions to address identified issues with negative profits in collusive scenarios.